Predicting and assessing the vitality of public urban spaces is crucial for effective urban design, aiming to prevent issues such as “ghost streets” and minimize resource wastage. However, existing assessment methods often lack temporal dynamics or heavily rely on historical big data, limiting their ability to accurately predict outcomes for unbuilt projects. To address these challenges, this study integrates previous methodologies with observations of crowd characteristics in public spaces. It introduces the crowd-frequency hypothesis and develops an algorithm to establish a time-dimensional urban vitality dynamic prediction model. Through a case study of the Rundle Mall neighborhood in Adelaide, Australia, the effectiveness of the prediction model was validated using on-site observation sampling and comparative verification. The prediction model framework allows for the determination of urban vitality within specific time ranges by directly inputting basic information, providing valuable support to urban planners and government officials during the design and decision-making processes. It offers a cost-effective approach to achieve sustainable urban vitality construction. Furthermore, machine learning techniques, specifically the decision tree model, were applied to case data to develop a set of preliminary algorithm tools, which enable output of reference urban vitality levels (high-medium-low).

Existing methods for generating 2D plans based on intelligent systems usually require human-defined rules, and their operations are complex. GANs can solve these problems through independent research and learning. However, they only have generative design research based on a single constraint condition, and whether they can generate a qualified design scheme under many constraints is still unclear. Therefore, this paper develops the M-StruGAN generative model based on the structural design framework of a GAN. Its application research is extended to the 2D-plan layout generation of homestay based on the constraints of hybrid structures, and the feasibility of the method is comprehensively verified through three aspects: image synthesis quality assessment, scheme rationality assessment, and scheme design quality assessment. Experimental results show that the quality of the drawings generated by M-StruGAN is qualified, designers have a high degree of acceptance of the design results of M-StruGAN, and M-StruGAN completed the learning of the critical points of the 2D layout. Finally, through the human–computer interaction application of M-StruGAN, it can be found that compared with traditional design methods, M-StruGAN based on pix2pixHD has high-definition image quality, higher design efficiency, lower design cost, and more stable design quality.

Over the past decade, biophilic architecture has been widely developed across the Asia–Pacific region. However, there is a notable lack of research based on online public reviews focusing on mid- to high-rise biophilic buildings, especially quantitative studies combining traditional architectural design features. This study aims to fill this gap by analyzing the typical floor plans and online public reviews of nine renowned biophilic buildings in the Asia–Pacific region. Using space syntax and natural language processing tools, the design features of typical floor plans and public feedback will be analyzed separately, and their correlation will be evaluated. Additionally, the content of negative and low-score reviews will be categorized to identify issues in current biophilic building designs. The findings suggest that biophilic design can stimulate widespread public discussion, with large direct blue–green elements receiving overwhelming attention. However, biophilic elements can also lead to negative sentiments due to factors like humidity, high temperatures, mold, and insects. This study provides insights and design recommendations for future biophilic buildings, demonstrating the value of biophilic design in public reviews and emphasizing the need to balance these factors to enhance public satisfaction and acceptance.

With the rise in cultural tourism, visitors’ demand for historical and cultural experiences has grown, prompting historical architecture not only to focus on physical preservation but also to offer more intuitive, engaging, and interactive experiences. This study proposes a design method for real-space gamified interactive experiences through mobile applications in historical environments. Qinghui Garden, one of the Four Famous Lingnan Gardens, is used as a case study. A total of 54 visitors participated in an on-site field experiment, with data collected through pre- and post-experience questionnaires, behavioral tracking, and supplementary semi-structured interviews. Through a comparative experiment with three groups of visitors—free exploration, traditional guided tours, and real-space gamified interactive experiences—it was found that the gamified interactive method demonstrated superior clarity and reliability in its technology. Visitor cognitive performance and subjective satisfaction increased by approximately 62.5%, and the gamified interaction effectively guided the spatial flow and interaction behaviors with specific spaces. These findings provide new insights into the design of real-space gamified interactive experiences in historical spaces, contributing significantly to the preservation and cognition of cultural heritage.